Summary of Efficient Reinforcement Learning For Routing Jobs in Heterogeneous Queueing Systems, by Neharika Jali et al.
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
by Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning algorithm, ACHQ, is proposed to efficiently route jobs to heterogeneous servers. In a one-fast-one-slow system, a threshold policy has been shown to be optimal, but this is not known for systems with multiple servers. While Reinforcement Learning (RL) can learn policies, the problem’s exponentially large state space size makes standard RL inefficient. ACHQ uses a low-dimensional soft threshold policy parameterization that leverages the queueing structure, providing guarantees of stationary-point convergence and approximate global optimality for two-server systems. Simulations demonstrate an improvement in expected response time of up to 30% compared to a greedy policy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re trying to find a way to send jobs to servers quickly and efficiently when there are many different types of servers. Right now, we don’t know the best way to do this for systems with multiple servers. A new algorithm called ACHQ is being developed that uses machine learning techniques to make good decisions. It’s designed to work well even when there are many possible choices. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
